When and Where Localization Fails: An Analysis of the Iterative Closest Point in Evolving Environment
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| Název: | When and Where Localization Fails: An Analysis of the Iterative Closest Point in Evolving Environment |
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| Autoři: | Dannaoui, Abdel-Raouf, Laconte, Johann, Debain, Christophe, Pomerleau, Francois, Checchin, Paul |
| Přispěvatelé: | Dannaoui, Abdel-Raouf |
| Zdroj: | 2025 European Conference on Mobile Robots (ECMR). :1-7 |
| Publication Status: | Preprint |
| Informace o vydavateli: | IEEE, 2025. |
| Rok vydání: | 2025 |
| Témata: | FOS: Computer and information sciences, Localization, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], ICP, Robotics, Robotics (cs.RO), Dataset |
| Popis: | Robust relocalization in dynamic outdoor environments remains a key challenge for autonomous systems relying on 3D lidar. While long-term localization has been widely studied, short-term environmental changes, occurring over days or weeks, remain underexplored despite their practical significance. To address this gap, we present a highresolution, short-term multi-temporal dataset collected weekly from February to April 2025 across natural and semi-urban settings. Each session includes high-density point cloud maps, 360 deg panoramic images, and trajectory data. Projected lidar scans, derived from the point cloud maps and modeled with sensor-accurate occlusions, are used to evaluate alignment accuracy against the ground truth using two Iterative Closest Point (ICP) variants: Point-to-Point and Point-to-Plane. Results show that Point-to-Plane offers significantly more stable and accurate registration, particularly in areas with sparse features or dense vegetation. This study provides a structured dataset for evaluating short-term localization robustness, a reproducible framework for analyzing scan-to-map alignment under noise, and a comparative evaluation of ICP performance in evolving outdoor environments. Our analysis underscores how local geometry and environmental variability affect localization success, offering insights for designing more resilient robotic systems. 7 pages, 7 figures, proceedings in European Conference on Mobile Robots (ECMR) 2025 |
| Druh dokumentu: | Article Conference object |
| Popis souboru: | application/pdf |
| DOI: | 10.1109/ecmr65884.2025.11162975 |
| DOI: | 10.48550/arxiv.2507.17531 |
| Přístupová URL adresa: | http://arxiv.org/abs/2507.17531 https://hal.inrae.fr/hal-05173203v1 |
| Rights: | STM Policy #29 CC BY |
| Přístupové číslo: | edsair.doi.dedup.....be91063cc697b5c342472c3fcb91d5d6 |
| Databáze: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | Robust relocalization in dynamic outdoor environments remains a key challenge for autonomous systems relying on 3D lidar. While long-term localization has been widely studied, short-term environmental changes, occurring over days or weeks, remain underexplored despite their practical significance. To address this gap, we present a highresolution, short-term multi-temporal dataset collected weekly from February to April 2025 across natural and semi-urban settings. Each session includes high-density point cloud maps, 360 deg panoramic images, and trajectory data. Projected lidar scans, derived from the point cloud maps and modeled with sensor-accurate occlusions, are used to evaluate alignment accuracy against the ground truth using two Iterative Closest Point (ICP) variants: Point-to-Point and Point-to-Plane. Results show that Point-to-Plane offers significantly more stable and accurate registration, particularly in areas with sparse features or dense vegetation. This study provides a structured dataset for evaluating short-term localization robustness, a reproducible framework for analyzing scan-to-map alignment under noise, and a comparative evaluation of ICP performance in evolving outdoor environments. Our analysis underscores how local geometry and environmental variability affect localization success, offering insights for designing more resilient robotic systems.<br />7 pages, 7 figures, proceedings in European Conference on Mobile Robots (ECMR) 2025 |
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| DOI: | 10.1109/ecmr65884.2025.11162975 |